Erythrocytic Pyruvate Kinase Mutations Causing Hemolytic Anemia, Osteosclerosis, and Secondary Hemochromatosis in Dogs
Bibliographic record
Abstract
BACKGROUND: Erythrocytic pyruvate kinase (PK) deficiency, first documented in Basenjis, is the most common inherited erythroenzymopathy in dogs. OBJECTIVES: To report 3 new breed-specific PK-LR gene mutations and a retrospective survey of PK mutations in as mall and selected group of Beagles and West Highland White Terriers (WHWT). ANIMALS: Labrador Retrievers (2 siblings, 5 unrelated), Pugs (2 siblings, 1 unrelated), Beagles (39 anemic, 29 other),WHWTs (22 anemic, 226 nonanemic), Cairn Terrier (n = 1). METHODS: Exons of the PK-LR gene were sequenced from genomic DNA of young dogs (<2 years) with persistent highly regenerative hemolytic anemia. RESULTS: A nonsense mutation (c.799C>T) resulting in a premature stop codon was identified in anemic Labrador Retriever siblings that had osteosclerosis, high serum ferritin concentrations, and severe hepatic secondary hemochromatosis. Anemic Pug and Beagle revealed 2 different missense mutations (c.848T>C, c.994G>A, respectively) resulting in intolerable amino acid changes to protein structure and enzyme function. Breed-specific mutation tests were developed. Among the biased group of 248 WHWTs, 9% and 35% were homozygous (affected) and heterozygous, respectively, for the previously described mutation (mutant allele frequency 0.26). A PK-deficient Cairn Terrier had the same insertion mutation as the affected WHWTs. Of the selected group of 68 Beagles, 35% were PK-deficient and 3% were carriers (0.37). CONCLUSIONS AND CLINICAL IMPORTANCE: Erythrocytic PK deficiency is caused by different mutations in different dog breeds and causes chronic severe hemolytic anemia, hemosiderosis, and secondary hemochromatosis because of chronic hemolysis and, an as yet unexplained osteosclerosis. The newly developed breed-specific mutation assays simplify the diagnosis of PK deficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".